



'''simple docstring'''


import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
						from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image




class   __UpperCamelCase							:







	def __init__(     self			,					_lowerCAmelCase			,					_lowerCAmelCase=13			,					_lowerCAmelCase=64			,					_lowerCAmelCase=2			,					_lowerCAmelCase=3			,					_lowerCAmelCase=True			,					_lowerCAmelCase=True			,					_lowerCAmelCase=32			,					_lowerCAmelCase=5			,					_lowerCAmelCase=4			,					_lowerCAmelCase=37			,					_lowerCAmelCase="gelu"			,					_lowerCAmelCase=0.1			,					_lowerCAmelCase=0.1			,					_lowerCAmelCase=10			,					_lowerCAmelCase=0.02			,					_lowerCAmelCase=[1, 16, 4, 4]			,					_lowerCAmelCase=None			,					)     ->     Tuple:
							'''simple docstring'''






							lowercase   =				parent
							lowercase   =				batch_size
							lowercase   =				image_size
							lowercase   =				patch_size
							lowercase   =				num_channels
							lowercase   =				is_training
							lowercase   =				use_labels
							lowercase   =				hidden_size
							lowercase   =				num_hidden_layers
							lowercase   =				num_attention_heads
							lowercase   =				intermediate_size
							lowercase   =				hidden_act
							lowercase   =				hidden_dropout_prob
							lowercase   =				attention_probs_dropout_prob
							lowercase   =				type_sequence_label_size
							lowercase   =				initializer_range
							lowercase   =				scope
							lowercase   =				backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							lowercase   =				(self.image_size // 32) ** 2
							lowercase   =				num_patches + 1







	def 	_a (     self    )     ->     List[str]:
							'''simple docstring'''






							lowercase   =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							lowercase   =				None
							if self.use_labels:
													lowercase   =				ids_tensor([self.batch_size]			,					self.type_sequence_label_size    )

							lowercase   =				self.get_config()

							return config, pixel_values, labels







	def 	_a (     self    )     ->     Tuple:
							'''simple docstring'''






							lowercase   =				{
							    """global_padding""": """same""",
							    """layer_type""": """bottleneck""",
							    """depths""": [3, 4, 9],
							    """out_features""": ["""stage1""", """stage2""", """stage3"""],
							    """embedding_dynamic_padding""": True,
							    """hidden_sizes""": [4, 8, 16, 32],
							    """num_groups""": 2,
							}

							return ViTHybridConfig(
							    image_size=self.image_size			,					patch_size=self.patch_size			,					num_channels=self.num_channels			,					hidden_size=self.hidden_size			,					num_hidden_layers=self.num_hidden_layers			,					num_attention_heads=self.num_attention_heads			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					is_decoder=snake_case__			,					initializer_range=self.initializer_range			,					backbone_featmap_shape=self.backbone_featmap_shape			,					backbone_config=snake_case__			,					)







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase    )     ->     int:
							'''simple docstring'''






							lowercase   =				ViTHybridModel(config=snake_case__    )
							model.to(snake_case__    )
							model.eval()
							lowercase   =				model(snake_case__    )
							self.parent.assertEqual(result.last_hidden_state.shape			,					(self.batch_size, self.seq_length, self.hidden_size)    )







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase    )     ->     List[Any]:
							'''simple docstring'''






							lowercase   =				self.type_sequence_label_size
							lowercase   =				ViTHybridForImageClassification(snake_case__    )
							model.to(snake_case__    )
							model.eval()
							lowercase   =				model(snake_case__			,					labels=snake_case__    )
							self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.type_sequence_label_size)    )







	def 	_a (     self    )     ->     int:
							'''simple docstring'''






							lowercase   =				self.prepare_config_and_inputs()
							lowercase  , lowercase  , lowercase   =				config_and_inputs
							lowercase   =				{"""pixel_values""": pixel_values}
							return config, inputs_dict




@require_torch
class   __UpperCamelCase							(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , unittest.TestCase      ):
	__A      	=       (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
	__A      	=       (
	    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
	    if is_torch_available()
	    else {}
	)
	__A      	=       False
	__A      	=       False
	__A      	=       False







	def 	_a (     self    )     ->     Any:
							'''simple docstring'''






							lowercase   =				ViTHybridModelTester(self    )
							lowercase   =				ConfigTester(self			,					config_class=snake_case__			,					has_text_modality=snake_case__			,					hidden_size=37    )







	def 	_a (     self    )     ->     List[str]:
							'''simple docstring'''






							self.config_tester.run_common_tests()







	@unittest.skip(reason="""ViT does not use inputs_embeds"""    )
	def 	_a (     self    )     ->     Optional[int]:
							'''simple docstring'''






							pass







	def 	_a (     self    )     ->     Dict:
							'''simple docstring'''






							lowercase  , lowercase   =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase   =				model_class(snake_case__    )
													self.assertIsInstance(model.get_input_embeddings()			,					(nn.Module)    )
													lowercase   =				model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(snake_case__			,					nn.Linear    )    )







	def 	_a (     self    )     ->     Dict:
							'''simple docstring'''






							lowercase  , lowercase   =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase   =				model_class(snake_case__    )
													lowercase   =				inspect.signature(model.forward    )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase   =				[*signature.parameters.keys()]

													lowercase   =				["""pixel_values"""]
													self.assertListEqual(arg_names[:1]			,					snake_case__    )







	def 	_a (     self    )     ->     List[str]:
							'''simple docstring'''






							lowercase   =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__    )







	def 	_a (     self    )     ->     Tuple:
							'''simple docstring'''






							lowercase   =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case__    )







	def 	_a (     self    )     ->     List[Any]:
							'''simple docstring'''






							lowercase  , lowercase   =				self.model_tester.prepare_config_and_inputs_for_common()

							lowercase   =				_config_zero_init(snake_case__    )
							for model_class in self.all_model_classes:
													lowercase   =				model_class(config=snake_case__    )
													# Skip the check for the backbone
													for name, module in model.named_modules():
																			if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																									lowercase   =				[F"""{name}.{key}""" for key in module.state_dict().keys()]
																									break

													for name, param in model.named_parameters():
																			if param.requires_grad:
																									if name in backbone_params:
																															continue
																									self.assertIn(
																									    ((param.data.mean() * 1E9).round() / 1E9).item()			,					[0.0, 1.0]			,					msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""			,					)







	@slow
	def 	_a (     self    )     ->     Union[str, Any]:
							'''simple docstring'''






							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase   =				ViTHybridModel.from_pretrained(snake_case__    )
													self.assertIsNotNone(snake_case__    )







def   SCREAMING_SNAKE_CASE					(					):
						lowercase   =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
						return image




@require_torch
@require_vision
class   __UpperCamelCase							(unittest.TestCase      ):







	@cached_property
	def 	_a (     self    )     ->     Any:
							'''simple docstring'''






							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
							    if is_vision_available()
							    else None
							)







	@slow
	def 	_a (     self    )     ->     Tuple:
							'''simple docstring'''






							lowercase   =				ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(
							    snake_case__    )

							lowercase   =				self.default_image_processor
							lowercase   =				prepare_img()
							lowercase   =				image_processor(images=snake_case__			,					return_tensors="""pt"""    ).to(snake_case__    )

							# forward pass
							with torch.no_grad():
													lowercase   =				model(**snake_case__    )

							# verify the logits
							lowercase   =				torch.Size((1, 1000)    )
							self.assertEqual(outputs.logits.shape			,					snake_case__    )

							lowercase   =				torch.tensor([-1.9090, -0.4993, -0.2389]    ).to(snake_case__    )

							self.assertTrue(torch.allclose(outputs.logits[0, :3]			,					snake_case__			,					atol=1E-4    )    )







	@slow
	@require_accelerate
	def 	_a (     self    )     ->     str:
							'''simple docstring'''






							lowercase   =				ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""    )
							lowercase   =				ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""			,					device_map="""auto"""    )

							lowercase   =				prepare_img()

							lowercase   =				image_processor(images=snake_case__			,					return_tensors="""pt"""    )
							lowercase   =				model(**snake_case__    )
							lowercase   =				outputs.logits
							# model predicts one of the 1000 ImageNet classes
							lowercase   =				logits.argmax(-1    ).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx]			,					"""tabby, tabby cat"""    )



from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   42
		lowerCAmelCase_     =   42





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     int     ,  snake_case__     :     int		)      ->	Any:
				_lowerCamelCase				   =  [[] for _ in range(snake_case__		)]
				_lowerCamelCase				   =  size
		def __getitem__(       self     :     Dict     ,  snake_case__     :     int		)      ->	Iterator[Edge]:
				return iter(self._graph[vertex]		)
		@property
		def   _snake_case       (       self     :     str		)      ->	int:
				return self._size
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				if weight not in (0, 1):
						raise ValueError('Edge weight must be either 0 or 1.'		)

				if to_vertex < 0 or to_vertex >= self.size:
						raise ValueError('Vertex indexes must be in [0; size).'		)

				self._graph[from_vertex].append(Edge(snake_case__     ,  snake_case__		)		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	int | None:
				_lowerCamelCase				   =  deque([start_vertex]		)
				_lowerCamelCase				   =  [None] * self.size
				_lowerCamelCase				   =  0

				while queue:
						_lowerCamelCase				   =  queue.popleft()
						_lowerCamelCase				   =  distances[current_vertex]
						if current_distance is None:
								continue

						for edge in self[current_vertex]:
								_lowerCamelCase				   =  current_distance + edge.weight
								_lowerCamelCase				   =  distances[edge.destination_vertex]
								if (
								    isinstance(snake_case__     ,  snake_case__		)
								    and new_distance >= dest_vertex_distance
								):
										continue
								_lowerCamelCase				   =  new_distance
								if edge.weight == 0:
										queue.appendleft(edge.destination_vertex		)
								else:
										queue.append(edge.destination_vertex		)

				if distances[finish_vertex] is None:
						raise ValueError('No path from start_vertex to finish_vertex.'		)

				return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()